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Experimental study of dynamic loss mechanisms in transonic turbines

Experimental study of dynamic loss mechanisms in transonic turbines
跨音速涡轮机动态损失机制的实验研究
批准号:
494817-2016
负责人:
Steinberg, Adam
金额:
$4.56万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

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中文摘要
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英文摘要
This project will enable gas turbines engines with improved efficiency and reduced weight. These engines areubiquitous in aviation, and also form a large and growing portion of the energy landscape. Both of the projectobjectives will ultimately result in less fuel required to fly or produce energy, and hence reduced emissions,greater sustainability, and lower operating costs.Specifically, this project focuses on mechanisms causing efficiency losses in transonic turbines. Transonicturbine stages can achieve greater work extraction than subsonic stages, and hence can potentially result infewer required stages and reduced weight, but also typically are less efficient due to various fluid mechanicphenomena. Of particular importance is the dynamics of periodic wakes shed from upstream rows of blades onthe flow inside a subsequent blade row. This project therefore will explicitly measure the loss mechanismsoccurring inside transonic turbine blades due to the passage of wakes.To do so, particle image velocimetry (PIV) will be applied in a large transonic linear cascade facility thathas been equipped with a high-speed moving bar apparatus. The facility, located at the GE Global ResearchCenter, can replicate the conditions found in real jet engines, but in a manner that is tractable for advancedmeasurements. Several experimental campaigns will be performed by University of Toronto staff and studentsat this facility.The project involves several innovative aspects. Considerable developmental work will be done on the PIVdiagnostics to enable measurement of the full dynamic velocity range found in the experiments, and to obtainmeasurements in the boundary layers near the turbine blades. Furthermore, new data mining techniques will beemployed to explicitly calculate transport of turbulence kinetic energy, and hence identify regions contributingto efficiency losses.
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